sessionInfo()Biostat 203B Homework 3
Due Feb 23 @ 11:59PM
Display machine information for reproducibility:
Load necessary libraries (you can add more as needed).
library(arrow)
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timestamplibrary(memuse)
library(pryr)
library(R.utils)Loading required package: R.ooLoading required package: R.methodsS3R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help.R.oo v1.25.0 (2022-06-12 02:20:02 UTC) successfully loaded. See ?R.oo for help.
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cat, commandArgs, getOption, isOpen, nullfile, parse, warningslibrary(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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combineDisplay your machine memory.
memuse::Sys.meminfo()In this exercise, we use tidyverse (ggplot2, dplyr, etc) to explore the MIMIC-IV data introduced in homework 1 and to build a cohort of ICU stays.
Q1. Visualizing patient trajectory
Visualizing a patient’s encounters in a health care system is a common task in clinical data analysis. In this question, we will visualize a patient’s ADT (admission-discharge-transfer) history and ICU vitals in the MIMIC-IV data.
Q1.1 ADT history
A patient’s ADT history records the time of admission, discharge, and transfer in the hospital. This figure shows the ADT history of the patient with subject_id 10001217 in the MIMIC-IV data. The x-axis is the calendar time, and the y-axis is the type of event (ADT, lab, procedure). The color of the line segment represents the care unit. The size of the line segment represents whether the care unit is an ICU/CCU. The crosses represent lab events, and the shape of the dots represents the type of procedure. The title of the figure shows the patient’s demographic information and the subtitle shows top 3 diagnoses.
Do a similar visualization for the patient with subject_id 10013310 using ggplot.
Hint: We need to pull information from data files patients.csv.gz, admissions.csv.gz, transfers.csv.gz, labevents.csv.gz, procedures_icd.csv.gz, diagnoses_icd.csv.gz, d_icd_procedures.csv.gz, and d_icd_diagnoses.csv.gz. For the big file labevents.csv.gz, use the Parquet format you generated in Homework 2. For reproducibility, make the Parquet folder labevents_pq available at the current working directory hw3, for example, by a symbolic link. Make your code reproducible.
ln -s /Users/zihengzhang/Downloads/203B/203b-hw/hw2/labevents_parquet \
./labevents_pqpatient_id <- 10013310 # set patient ID
# find race for that patient
race_info <- read_csv("~/mimic/hosp/admissions.csv.gz") |>
filter(subject_id == patient_id) |>
select(subject_id, race)Rows: 431231 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): admission_type, admit_provider_id, admission_location, discharge_l...
dbl (3): subject_id, hadm_id, hospital_expire_flag
dttm (5): admittime, dischtime, deathtime, edregtime, edouttime
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# find other personal info for that patient
personal_info <- read_csv("~/mimic/hosp/patients.csv.gz") |>
filter(subject_id == patient_id) |>
select(subject_id, gender, anchor_age) |>
rename(age = anchor_age) |>
left_join(race_info, by = "subject_id") |>
distinct()Rows: 299712 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): gender, anchor_year_group
dbl (3): subject_id, anchor_age, anchor_year
date (1): dod
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# find top 3 diagnoses for that patient in each stay ID
diagonose_code_info <- read_csv("~/mimic/hosp/d_icd_diagnoses.csv.gz")Rows: 109775 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): icd_code, long_title
dbl (1): icd_version
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.diagnose_info <- read_csv("~/mimic/hosp/diagnoses_icd.csv.gz") |>
filter(subject_id == patient_id) |>
select(-icd_version) |>
filter(seq_num <= 3) |>
left_join(diagonose_code_info, by = "icd_code") |>
select(-icd_version)Rows: 4756326 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): icd_code
dbl (4): subject_id, hadm_id, seq_num, icd_version
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# find ADT info for that patient
ADT_info <- read_csv("~/mimic/hosp/transfers.csv.gz") |>
filter(subject_id == patient_id) |>
select(subject_id, hadm_id, intime, outtime, eventtype, careunit) |>
filter(eventtype != "discharge") |>
arrange(intime) |>
distinct()Rows: 1890972 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): eventtype, careunit
dbl (3): subject_id, hadm_id, transfer_id
dttm (2): intime, outtime
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# find lab info for that patient
lab_info <- open_dataset("./labevents_pq", format = "parquet") |>
filter(subject_id == patient_id) |>
select(subject_id, hadm_id, charttime) |>
collect() |>
distinct()
# find procedure info for that patient
procedure_code_info <- read_csv("~/mimic/hosp/d_icd_procedures.csv.gz")Rows: 85257 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): icd_code, long_title
dbl (1): icd_version
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.procedure_info <- read_csv("~/mimic/hosp/procedures_icd.csv.gz") |>
filter(subject_id == patient_id) |>
select(subject_id, hadm_id, chartdate, icd_code) |>
mutate(chartdate = as.POSIXct(chartdate, format = "%Y-%m-%d %H:%M:%S")) |>
left_join(procedure_code_info, by = "icd_code") |>
select(-icd_version) |>
arrange(chartdate) |>
# split long title and only keep the first part
mutate(short_title = str_split(long_title, ",") %>% sapply(function(x)
x[1])) |>
distinct()Rows: 669186 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): icd_code
dbl (4): subject_id, hadm_id, seq_num, icd_version
date (1): chartdate
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# combine personal info into required format
title_text <- sprintf("Patient %d, %s, %d years old, %s",
personal_info$subject_id, personal_info$gender,
personal_info$age, tolower(personal_info$race))
top3_text <- diagnose_info |>
slice(1:3) |>
select(long_title) |>
mutate(first_five_words = str_split(long_title, "\\s+") %>%
sapply(function(x) {
if (length(x) < 5) {
paste(x, collapse = " ") # in case some diagnosis has less than 5 words
} else {
paste(x[1:5], collapse = " ")
}
})) |>
pull(first_five_words)
subtitle_text <- sprintf("%s\n%s\n%s", top3_text[1], top3_text[2], top3_text[3])
# create the plot and y-axis
empty_plot <- ggplot() +
geom_blank() +
scale_y_discrete(limits = c("Procedure", "Lab", "ADT")) +
labs(x = "Calendar Time", title = title_text,
subtitle = tolower(subtitle_text)) +
theme_bw()
ADT_plot <- empty_plot +
geom_segment(data = ADT_info, aes(x = intime, xend = outtime, y = "ADT",
yend = "ADT", color = careunit),
linewidth = ifelse(str_detect(ADT_info$careunit, "CCU") |
str_detect(ADT_info$careunit, "ICU"),
8, 3)) +
labs(color = "Care Unit") +
theme(legend.position = "bottom", legend.box = "vertical",
text = element_text(size = 8))
Lab_plot <- ADT_plot +
geom_point(data = lab_info, aes(x = charttime, y = "Lab"),
size = 5, shape = "+")
Procedure_plot <- Lab_plot +
geom_point(data = procedure_info, aes(x = chartdate, y = "Procedure",
shape = short_title), size = 5) +
# set manually the shape of the procedure to be the total 9 procedures
scale_shape_manual(values =
seq(1, length(unique(procedure_info$short_title)))) +
labs(shape = "Procedure", y = "") +
guides(shape = guide_legend(nrow = 5)) +
theme(legend.position = "bottom", legend.box = "vertical",
text = element_text(size = 8))
plot(Procedure_plot)
Answer: This patient has been admitted to the hospital three times. Each admission results in the top 3 diagnoses. Therefore, for the subtitle, we select one of the admission records and displayed the top 3 diagnoses for that admission. Here we select admission record with hadm_id = 21243435 and we only display the first few words of the long title because the long title is too long.
For the label of procedure, since the long title is too long, we only display the words before the first comma of the long title. We manually set the shape of the procedure to be the total 9 procedures and then all of the procedures are displayed in the plot.
Q1.2 ICU stays
ICU stays are a subset of ADT history. This figure shows the vitals of the patient 10001217 during ICU stays. The x-axis is the calendar time, and the y-axis is the value of the vital. The color of the line represents the type of vital. The facet grid shows the abbreviation of the vital and the stay ID.
Do a similar visualization for the patient 10013310.
ln -s /Users/zihengzhang/Downloads/203B/203b-hw/hw2/chartevents.parquet \
./chartevents_pqpatient_id <- 10013310
item_icu_info <- read_csv("~/mimic/icu/d_items.csv.gz") |>
filter(itemid %in% c(220045, 220180, 220179, 220210, 223761)) |>
select(itemid, abbreviation) |>
distinct()Rows: 4014 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): label, abbreviation, linksto, category, unitname, param_type
dbl (3): itemid, lownormalvalue, highnormalvalue
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.chartevents_info <- open_dataset("./chartevents_pq", format = "parquet") |>
filter(subject_id == patient_id) |>
filter(itemid %in% c(220045, 220180, 220179, 220210, 223761)) |>
select(subject_id, stay_id, itemid, charttime, valuenum) |>
collect() |>
left_join(item_icu_info, by = "itemid") |>
mutate(charttime = with_tz(charttime, "UTC"))
title_text <- sprintf("Patient %d ICU stays - Vitals",
chartevents_info$subject_id)
icu_plot <- ggplot(chartevents_info, aes(x = charttime, y = valuenum,
color = abbreviation)) +
geom_point(size = 1) +
geom_line() +
labs(title = title_text,
x = "",
y = "") +
facet_grid(rows = vars(abbreviation), cols = vars(stay_id), scales = "free") +
theme_light() +
theme(legend.position = "none") +
scale_x_datetime(date_labels = "%b %d %H:%M") +
guides(x = guide_axis(n.dodge = 2)) # to avoid overlapping of x-axis labels
plot(icu_plot)
Q2. ICU stays
icustays.csv.gz (https://mimic.mit.edu/docs/iv/modules/icu/icustays/) contains data about Intensive Care Units (ICU) stays. The first 10 lines are
zcat < ~/mimic/icu/icustays.csv.gz | headsubject_id,hadm_id,stay_id,first_careunit,last_careunit,intime,outtime,los
10000032,29079034,39553978,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2180-07-23 14:00:00,2180-07-23 23:50:47,0.4102662037037037
10000980,26913865,39765666,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2189-06-27 08:42:00,2189-06-27 20:38:27,0.4975347222222222
10001217,24597018,37067082,Surgical Intensive Care Unit (SICU),Surgical Intensive Care Unit (SICU),2157-11-20 19:18:02,2157-11-21 22:08:00,1.1180324074074075
10001217,27703517,34592300,Surgical Intensive Care Unit (SICU),Surgical Intensive Care Unit (SICU),2157-12-19 15:42:24,2157-12-20 14:27:41,0.9481134259259258
10001725,25563031,31205490,Medical/Surgical Intensive Care Unit (MICU/SICU),Medical/Surgical Intensive Care Unit (MICU/SICU),2110-04-11 15:52:22,2110-04-12 23:59:56,1.338587962962963
10001884,26184834,37510196,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2131-01-11 04:20:05,2131-01-20 08:27:30,9.171817129629629
10002013,23581541,39060235,Cardiac Vascular Intensive Care Unit (CVICU),Cardiac Vascular Intensive Care Unit (CVICU),2160-05-18 10:00:53,2160-05-19 17:33:33,1.3143518518518518
10002155,20345487,32358465,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2131-03-09 21:33:00,2131-03-10 18:09:21,0.8585763888888889
10002155,23822395,33685454,Coronary Care Unit (CCU),Coronary Care Unit (CCU),2129-08-04 12:45:00,2129-08-10 17:02:38,6.178912037037037Q2.1 Ingestion
Import icustays.csv.gz as a tibble icustays_tble.
icustays_tble <- read_csv("~/mimic/icu/icustays.csv.gz")Rows: 73181 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): first_careunit, last_careunit
dbl (4): subject_id, hadm_id, stay_id, los
dttm (2): intime, outtime
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Q2.2 Summary and visualization
How many unique subject_id? Can a subject_id have multiple ICU stays? Summarize the number of ICU stays per subject_id by graphs.
# calculate the number of unique subject_id
icustays_tble |> distinct(subject_id) |> count()# A tibble: 1 × 1
n
<int>
1 50920icustays_tble |> count(subject_id) # A tibble: 50,920 × 2
subject_id n
<dbl> <int>
1 10000032 1
2 10000980 1
3 10001217 2
4 10001725 1
5 10001884 1
6 10002013 1
7 10002155 3
8 10002348 1
9 10002428 4
10 10002430 1
# ℹ 50,910 more rowsAnswer: There are 50920 unique subject_id. A subject_id can have multiple ICU stays.
icustays_tble |> count(subject_id) |>
ggplot() +
geom_bar(mapping = aes(x = n)) +
labs(title = "Number of ICU stays per subject_id") +
xlab("ICU Stays") +
ylab("Number of subject_id") +
theme_bw()
Answer: The number of ICU stays per subject_id is shown in the graph above. Most subject_id have 1 ICU stay, and the number of subject_id decreases as the number of ICU stays increases.
Q3. admissions data
Information of the patients admitted into hospital is available in admissions.csv.gz. See https://mimic.mit.edu/docs/iv/modules/hosp/admissions/ for details of each field in this file. The first 10 lines are
zcat < ~/mimic/hosp/admissions.csv.gz | headsubject_id,hadm_id,admittime,dischtime,deathtime,admission_type,admit_provider_id,admission_location,discharge_location,insurance,language,marital_status,race,edregtime,edouttime,hospital_expire_flag
10000032,22595853,2180-05-06 22:23:00,2180-05-07 17:15:00,,URGENT,P874LG,TRANSFER FROM HOSPITAL,HOME,Other,ENGLISH,WIDOWED,WHITE,2180-05-06 19:17:00,2180-05-06 23:30:00,0
10000032,22841357,2180-06-26 18:27:00,2180-06-27 18:49:00,,EW EMER.,P09Q6Y,EMERGENCY ROOM,HOME,Medicaid,ENGLISH,WIDOWED,WHITE,2180-06-26 15:54:00,2180-06-26 21:31:00,0
10000032,25742920,2180-08-05 23:44:00,2180-08-07 17:50:00,,EW EMER.,P60CC5,EMERGENCY ROOM,HOSPICE,Medicaid,ENGLISH,WIDOWED,WHITE,2180-08-05 20:58:00,2180-08-06 01:44:00,0
10000032,29079034,2180-07-23 12:35:00,2180-07-25 17:55:00,,EW EMER.,P30KEH,EMERGENCY ROOM,HOME,Medicaid,ENGLISH,WIDOWED,WHITE,2180-07-23 05:54:00,2180-07-23 14:00:00,0
10000068,25022803,2160-03-03 23:16:00,2160-03-04 06:26:00,,EU OBSERVATION,P51VDL,EMERGENCY ROOM,,Other,ENGLISH,SINGLE,WHITE,2160-03-03 21:55:00,2160-03-04 06:26:00,0
10000084,23052089,2160-11-21 01:56:00,2160-11-25 14:52:00,,EW EMER.,P6957U,WALK-IN/SELF REFERRAL,HOME HEALTH CARE,Medicare,ENGLISH,MARRIED,WHITE,2160-11-20 20:36:00,2160-11-21 03:20:00,0
10000084,29888819,2160-12-28 05:11:00,2160-12-28 16:07:00,,EU OBSERVATION,P63AD6,PHYSICIAN REFERRAL,,Medicare,ENGLISH,MARRIED,WHITE,2160-12-27 18:32:00,2160-12-28 16:07:00,0
10000108,27250926,2163-09-27 23:17:00,2163-09-28 09:04:00,,EU OBSERVATION,P38XXV,EMERGENCY ROOM,,Other,ENGLISH,SINGLE,WHITE,2163-09-27 16:18:00,2163-09-28 09:04:00,0
10000117,22927623,2181-11-15 02:05:00,2181-11-15 14:52:00,,EU OBSERVATION,P2358X,EMERGENCY ROOM,,Other,ENGLISH,DIVORCED,WHITE,2181-11-14 21:51:00,2181-11-15 09:57:00,0Q3.1 Ingestion
Import admissions.csv.gz as a tibble admissions_tble.
admissions_tble <- read_csv("~/mimic/hosp/admissions.csv.gz")Rows: 431231 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): admission_type, admit_provider_id, admission_location, discharge_l...
dbl (3): subject_id, hadm_id, hospital_expire_flag
dttm (5): admittime, dischtime, deathtime, edregtime, edouttime
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Q3.2 Summary and visualization
Summarize the following information by graphics and explain any patterns you see.
- number of admissions per patient
- admission hour (anything unusual?)
- admission minute (anything unusual?)
- length of hospital stay (from admission to discharge) (anything unusual?)
According to the MIMIC-IV documentation,
All dates in the database have been shifted to protect patient confidentiality. Dates will be internally consistent for the same patient, but randomly distributed in the future. Dates of birth which occur in the present time are not true dates of birth. Furthermore, dates of birth which occur before the year 1900 occur if the patient is older than 89. In these cases, the patient’s age at their first admission has been fixed to 300.
admissions_tble |> distinct(subject_id) |> count()# A tibble: 1 × 1
n
<int>
1 180733admissions_tble |> count(subject_id)# A tibble: 180,733 × 2
subject_id n
<dbl> <int>
1 10000032 4
2 10000068 1
3 10000084 2
4 10000108 1
5 10000117 2
6 10000248 1
7 10000280 1
8 10000560 1
9 10000635 1
10 10000719 1
# ℹ 180,723 more rowsAnswer: There are 180733 unique subject_id. A subject_id can have multiple admissions.
admissions_tble |> count(subject_id) |>
ggplot() +
geom_histogram(mapping = aes(x = n), binwidth = 5, fill = "salmon",
color = "black") +
labs(title = "Admissions vs Subject ID") +
xlab("Number of Admissions") +
ylab("Count") +
theme_bw() +
scale_y_continuous(labels = scales::comma)
Answer: The number of admissions per subject_id is shown in the graph above. Most subject_id have 1 admission, and the number of subject_id decreases as the number of admissions increases.
# extract the hour from admittime
admissions_hour <- hour(admissions_tble$admittime) |> as_tibble()
ggplot(data = admissions_hour) +
geom_bar(mapping = aes(x = value)) +
labs(title = "Distribution of Admission Hour") +
xlab("Hour") +
ylab("Count") +
theme_bw()
Answer: The admission hour is shown in the graph above. It seems there is something unusual. The number of admissions is highest at midnight, gradually decreasing over time. However, there is a sudden increase in admissions at 7AM. Then, as time progresses, the number of admissions gradually increases. The evening and night are the peak admission times within a day, with the overall situation being relatively balanced.
# extract the minute from admittime
admissions_minute <- minute(admissions_tble$admittime) |> as_tibble()
ggplot(data = admissions_minute) +
geom_bar(mapping = aes(x = value)) +
labs(title = "Distribution of Admission Minute") +
xlab("Minute") +
ylab("Count") +
theme_bw()
Answer: The admission minute is shown in the graph above. There are very clear unusual patterns. There are significant peaks in admissions at the top of the hour, 15 minutes past, 30 minutes past, and 45 minutes past the hour. At other times, the number of admissions is relatively evenly distributed.
# calculate the length of hospital stay
admissions_length <- interval(admissions_tble$dischtime,
admissions_tble$admittime) |> hour() |> as_tibble()Warning in class(xx) <- cl: Setting class(x) to multiple strings ("POSIXct",
"POSIXt", ...); result will no longer be an S4 objectWarning: tz(): Don't know how to compute timezone for object of class Interval;
returning "UTC".ggplot(admissions_length) +
geom_bar(mapping = aes(x = value)) +
labs(title = "Distribution of Length of Admission") +
xlab("Length") +
ylab("Count") +
theme_bw()
Answer: The length of hospital stay is shown in the graph above. It appears there are some unusual patterns. The most common length of hospital stay is around 3 hours. As the length of hospital stay increases, the frequency gradually decreases. The least common length is around 14 hours. However, as the length of hospital stay continues to increase, the frequency begins to gradually rise again. The longest length is around 24 hours.
Q4. patients data
Patient information is available in patients.csv.gz. See https://mimic.mit.edu/docs/iv/modules/hosp/patients/ for details of each field in this file. The first 10 lines are
zcat < ~/mimic/hosp/patients.csv.gz | headsubject_id,gender,anchor_age,anchor_year,anchor_year_group,dod
10000032,F,52,2180,2014 - 2016,2180-09-09
10000048,F,23,2126,2008 - 2010,
10000068,F,19,2160,2008 - 2010,
10000084,M,72,2160,2017 - 2019,2161-02-13
10000102,F,27,2136,2008 - 2010,
10000108,M,25,2163,2014 - 2016,
10000115,M,24,2154,2017 - 2019,
10000117,F,48,2174,2008 - 2010,
10000178,F,59,2157,2017 - 2019,Q4.1 Ingestion
Import patients.csv.gz (https://mimic.mit.edu/docs/iv/modules/hosp/patients/) as a tibble patients_tble.
patients_tble <- read_csv("~/mimic/hosp/patients.csv.gz")Rows: 299712 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): gender, anchor_year_group
dbl (3): subject_id, anchor_age, anchor_year
date (1): dod
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Q4.2 Summary and visualization
Summarize variables gender and anchor_age by graphics, and explain any patterns you see.
ggplot(patients_tble, aes(x = gender, fill = gender)) +
geom_bar() +
# show the count on top of the bar
geom_text(stat = "count", aes(label = after_stat(count)), vjust = -0.5,
color = "black", size = 3) +
labs(title = "Distribution of Gender",
x = "Gender",
y = "Count") +
theme_bw()
Answer: The number of female patients is slightly higher than the number of male patients. 158,553 vs 141,159.
ggplot(patients_tble, aes(x = anchor_age)) +
geom_histogram(binwidth = 5, color = "black", alpha = 0.7) +
labs(title = "Distribution of Anchor_Age",
x = "Anchor_Age",
y = "Count") +
theme_bw()
ggplot(patients_tble, aes(x = gender, y = anchor_age, fill = gender)) +
geom_violin() +
labs(title = "Anchor Age Distribution by Gender",
x = "Gender",
y = "Anchor Age")
Answer: The distribution of anchor_age is shown in the graph above. The number of patients is highest in the youngest age group, around 20 years old, and the number of patients firstly decreases and then increases as the age increases. There is another peak in the age group 55. And then the number of patients gradually decreases as the age increases. Next, we compare anchor_age in different gender groups. The distribution of anchor_age in different gender groups are almost the same. The largest number of age groups for both gender groups are around 20 years old and then the number of people decreases when they are 40 years old. There is a slight increase in numbers around 60. Finally, as age increases, the number of people decreases for both gender groups.
Q5. Lab results
labevents.csv.gz (https://mimic.mit.edu/docs/iv/modules/hosp/labevents/) contains all laboratory measurements for patients. The first 10 lines are
zcat < ~/mimic/hosp/labevents.csv.gz | headlabevent_id,subject_id,hadm_id,specimen_id,itemid,order_provider_id,charttime,storetime,value,valuenum,valueuom,ref_range_lower,ref_range_upper,flag,priority,comments
1,10000032,,45421181,51237,P28Z0X,2180-03-23 11:51:00,2180-03-23 15:15:00,1.4,1.4,,0.9,1.1,abnormal,ROUTINE,
2,10000032,,45421181,51274,P28Z0X,2180-03-23 11:51:00,2180-03-23 15:15:00,___,15.1,sec,9.4,12.5,abnormal,ROUTINE,VERIFIED.
3,10000032,,52958335,50853,P28Z0X,2180-03-23 11:51:00,2180-03-25 11:06:00,___,15,ng/mL,30,60,abnormal,ROUTINE,NEW ASSAY IN USE ___: DETECTS D2 AND D3 25-OH ACCURATELY.
4,10000032,,52958335,50861,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,102,102,IU/L,0,40,abnormal,ROUTINE,
5,10000032,,52958335,50862,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,3.3,3.3,g/dL,3.5,5.2,abnormal,ROUTINE,
6,10000032,,52958335,50863,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,109,109,IU/L,35,105,abnormal,ROUTINE,
7,10000032,,52958335,50864,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,___,8,ng/mL,0,8.7,,ROUTINE,MEASURED BY ___.
8,10000032,,52958335,50868,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,12,12,mEq/L,8,20,,ROUTINE,
9,10000032,,52958335,50878,P28Z0X,2180-03-23 11:51:00,2180-03-23 16:40:00,143,143,IU/L,0,40,abnormal,ROUTINE,d_labitems.csv.gz (https://mimic.mit.edu/docs/iv/modules/hosp/d_labitems/) is the dictionary of lab measurements.
zcat < ~/mimic/hosp/d_labitems.csv.gz | headitemid,label,fluid,category
50801,Alveolar-arterial Gradient,Blood,Blood Gas
50802,Base Excess,Blood,Blood Gas
50803,"Calculated Bicarbonate, Whole Blood",Blood,Blood Gas
50804,Calculated Total CO2,Blood,Blood Gas
50805,Carboxyhemoglobin,Blood,Blood Gas
50806,"Chloride, Whole Blood",Blood,Blood Gas
50808,Free Calcium,Blood,Blood Gas
50809,Glucose,Blood,Blood Gas
50810,"Hematocrit, Calculated",Blood,Blood GasWe are interested in the lab measurements of creatinine (50912), potassium (50971), sodium (50983), chloride (50902), bicarbonate (50882), hematocrit (51221), white blood cell count (51301), and glucose (50931). Retrieve a subset of labevents.csv.gz that only containing these items for the patients in icustays_tble. Further restrict to the last available measurement (by storetime) before the ICU stay. The final labevents_tble should have one row per ICU stay and columns for each lab measurement.
Hint: Use the Parquet format you generated in Homework 2. For reproducibility, make labevents_pq folder available at the current working directory hw3, for example, by a symbolic link.
d_item_subset <- read_csv("~/mimic/hosp/d_labitems.csv.gz") |>
select(itemid, label) # we only need itemid and its label nameRows: 1622 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): label, fluid, category
dbl (1): itemid
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.icustays_tble_subset <- select(icustays_tble, subject_id, stay_id, intime)
labevents_tble <- open_dataset("./labevents_pq", format = "parquet") |>
filter(itemid %in% c(50912, 50971, 50983, 50902,
50882, 51221, 51301, 50931)) |>
select(subject_id, itemid, valuenum, storetime) |>
arrange(subject_id, itemid) |>
collect() |>
# filter subject_id in labevents to match with icustays_tble
semi_join(icustays_tble_subset, by = c("subject_id")) |>
# combine label names
left_join(d_item_subset, by = c("itemid")) |>
# combine intime and stay_id by subject_id
left_join(icustays_tble_subset, by = c("subject_id")) |>
# filter the measurement before the ICU stay
filter(storetime < intime) |>
group_by(subject_id, label, stay_id) |>
# sort by storetime and get the last available measurement
arrange(storetime, .by_group = TRUE) |>
summarise(finalvalue = last(valuenum)) |>
mutate(label = tolower(label)) |>
ungroup() |>
# transform the table to wide format
pivot_wider(names_from = label, values_from = finalvalue) |>
rename("wbc" = "white blood cells")Warning in left_join(left_join(semi_join(collect(arrange(select(filter(open_dataset("./labevents_pq", : Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 845 of `x` matches multiple rows in `y`.
ℹ Row 1 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.`summarise()` has grouped output by 'subject_id', 'label'. You can override
using the `.groups` argument.print(labevents_tble, width = Inf)# A tibble: 68,467 × 10
subject_id stay_id bicarbonate chloride creatinine glucose hematocrit
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10000032 39553978 25 95 0.7 102 41.1
2 10000980 39765666 21 109 2.3 89 27.3
3 10001217 34592300 30 104 0.5 87 37.4
4 10001217 37067082 22 108 0.6 112 38.1
5 10001725 31205490 NA 98 NA NA NA
6 10001884 37510196 30 88 1.1 141 39.7
7 10002013 39060235 24 102 0.9 288 34.9
8 10002155 31090461 23 98 2.8 117 25.5
9 10002155 32358465 26 85 1.4 133 22.4
10 10002155 33685454 24 105 1.1 138 39.7
potassium sodium wbc
<dbl> <dbl> <dbl>
1 6.7 126 6.9
2 3.9 144 5.3
3 4.1 142 5.4
4 4.2 142 15.7
5 4.1 139 NA
6 4.5 130 12.2
7 3.5 137 7.2
8 4.9 135 17.9
9 5.7 120 9.8
10 4.6 139 7.9
# ℹ 68,457 more rowsQ6. Vitals from charted events
chartevents.csv.gz (https://mimic.mit.edu/docs/iv/modules/icu/chartevents/) contains all the charted data available for a patient. During their ICU stay, the primary repository of a patient’s information is their electronic chart. The itemid variable indicates a single measurement type in the database. The value variable is the value measured for itemid. The first 10 lines of chartevents.csv.gz are
zcat < ~/mimic/icu/chartevents.csv.gz | headd_items.csv.gz (https://mimic.mit.edu/docs/iv/modules/icu/d_items/) is the dictionary for the itemid in chartevents.csv.gz.
zcat < ~/mimic/icu/d_items.csv.gz | headWe are interested in the vitals for ICU patients: heart rate (220045), systolic non-invasive blood pressure (220179), diastolic non-invasive blood pressure (220180), body temperature in Fahrenheit (223761), and respiratory rate (220210). Retrieve a subset of chartevents.csv.gz only containing these items for the patients in icustays_tble. Further restrict to the first vital measurement within the ICU stay. The final chartevents_tble should have one row per ICU stay and columns for each vital measurement.
Hint: Use the Parquet format you generated in Homework 2. For reproducibility, make chartevents_pq folder available at the current working directory, for example, by a symbolic link. Answer: This procedure has been done in Q1.2.
item_icu_subset <- read_csv("~/mimic/icu/d_items.csv.gz") |>
select(itemid, label) |>
# change the label format into required format
mutate(label = str_replace_all(label, " ", "_")) |>
mutate(label = tolower(label))Rows: 4014 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): label, abbreviation, linksto, category, unitname, param_type
dbl (3): itemid, lownormalvalue, highnormalvalue
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.icustays_tble_subset <- select(icustays_tble, subject_id, stay_id, intime,
outtime)
chartevents_tble <- arrow::open_dataset("./chartevents_pq",
format = "parquet") |>
filter(itemid %in% c(220045, 220180, 220179, 220210, 223761)) |>
select(subject_id, itemid, valuenum, charttime) |>
arrange(subject_id, itemid) |>
collect() |>
semi_join(icustays_tble_subset, by = c("subject_id")) |>
left_join(item_icu_subset, by = c("itemid")) |>
left_join(icustays_tble_subset, by = c("subject_id")) |>
filter(charttime >= intime & charttime <= outtime) |>
group_by(subject_id, label, stay_id) |>
# sort by charttime and get the first available measurement
arrange(charttime, .by_group = TRUE) |>
summarise(firstvalue = first(valuenum)) |>
ungroup() |>
pivot_wider(names_from = label, values_from = firstvalue)Warning in left_join(left_join(semi_join(collect(arrange(select(filter(arrow::open_dataset("./chartevents_pq", : Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 91 of `x` matches multiple rows in `y`.
ℹ Row 1 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.`summarise()` has grouped output by 'subject_id', 'label'. You can override
using the `.groups` argument.# change the fourth and fifth column order to match the screenshot
temp <- chartevents_tble[, 4]
chartevents_tble[, 4] <- chartevents_tble[, 5]
chartevents_tble[, 5] <- temp
temp_colname <- colnames(chartevents_tble)[4]
colnames(chartevents_tble)[4] <- colnames(chartevents_tble)[5]
colnames(chartevents_tble)[5] <- temp_colname
print(chartevents_tble, width = Inf)# A tibble: 73,164 × 7
subject_id stay_id heart_rate non_invasive_blood_pressure_systolic
<dbl> <dbl> <dbl> <dbl>
1 10000032 39553978 91 84
2 10000980 39765666 77 150
3 10001217 34592300 96 167
4 10001217 37067082 86 151
5 10001725 31205490 55 73
6 10001884 37510196 38 180
7 10002013 39060235 80 104
8 10002155 31090461 94 118
9 10002155 32358465 98 109
10 10002155 33685454 68 126
non_invasive_blood_pressure_diastolic respiratory_rate temperature_fahrenheit
<dbl> <dbl> <dbl>
1 48 24 98.7
2 77 23 98
3 95 11 97.6
4 90 18 98.5
5 56 19 97.7
6 12 10 98.1
7 70 14 97.2
8 51 18 96.9
9 65 23 97.7
10 61 18 95.9
# ℹ 73,154 more rowsQ7. Putting things together
Let us create a tibble mimic_icu_cohort for all ICU stays, where rows are all ICU stays of adults (age at intime >= 18) and columns contain at least following variables
- all variables in
icustays_tble
- all variables in
admissions_tble
- all variables in
patients_tble - the last lab measurements before the ICU stay in
labevents_tble - the first vital measurements during the ICU stay in
chartevents_tble
The final mimic_icu_cohort should have one row per ICU stay and columns for each variable.
Answer: anchor_age is age at the anchor_year so we use year(intime) - anchor_year to calculate the age difference between the year of intime and anchor_year and then add it to anchor_age to get the age at intime.
mimic_icu_cohort <- icustays_tble |>
left_join(admissions_tble, by = c("subject_id", "hadm_id")) |>
left_join(patients_tble, by = c("subject_id")) |>
left_join(labevents_tble, by = c("subject_id", "stay_id")) |>
left_join(chartevents_tble, by = c("subject_id", "stay_id")) |>
mutate(age_at_intime = anchor_age + year(intime) - anchor_year) |>
filter(age_at_intime >= 18)Q8. Exploratory data analysis (EDA)
Summarize the following information about the ICU stay cohort mimic_icu_cohort using appropriate numerics or graphs:
Length of ICU stay
losvs demographic variables (race, insurance, marital_status, gender, age at intime)Length of ICU stay
losvs the last available lab measurements before ICU stayLength of ICU stay
losvs the average vital measurements within the first hour of ICU stayLength of ICU stay
losvs first ICU unit
summary_by_race <- mimic_icu_cohort |>
group_by(race) |>
summarise(
mean_los = mean(los),
sd_los = sd(los),
median_los = median(los),
min_los = min(los),
max_los = max(los)
)
print(summary_by_race, width = Inf)# A tibble: 33 × 6
race mean_los sd_los median_los min_los max_los
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 AMERICAN INDIAN/ALASKA NATIVE 4.46 6.54 2.09 0.0879 49.9
2 ASIAN 3.49 4.90 1.93 0.0631 59.5
3 ASIAN - ASIAN INDIAN 4.20 7.61 1.95 0.160 66.3
4 ASIAN - CHINESE 3.36 5.39 1.86 0.0206 77.7
5 ASIAN - KOREAN 4.23 6.35 2.08 0.211 39.4
6 ASIAN - SOUTH EAST ASIAN 3.04 5.42 1.82 0.0784 76.1
7 BLACK/AFRICAN 3.82 5.90 2.02 0.0345 54.8
8 BLACK/AFRICAN AMERICAN 3.28 4.80 1.85 0.00346 95.8
9 BLACK/CAPE VERDEAN 3.22 4.94 1.70 0.0296 51.7
10 BLACK/CARIBBEAN ISLAND 3.61 5.13 1.95 0.0445 38.2
# ℹ 23 more rowsggplot(summary_by_race, aes(x = race, y = mean_los)) +
geom_bar(stat = "identity", fill = "salmon", width = 0.5) +
labs(title = "Mean ICU Length of Stay by Race", x = "Race",
y = "Mean Length of Stay") +
coord_flip() +
theme_bw()
Comment: The mean lengths of ICU stay for all race groups are more than 2 days. The AMERICAN INDIAN/ALASKA NATIVE group has the highest mean length of stay, which is around 4.5 days, while the HISPANIC/LATINO - MEXICAN group has the lowest mean length of stay, which is more than 2.5 days.
ggplot(mimic_icu_cohort, aes(los, fill = insurance)) +
geom_histogram(color = "black", ) +
facet_wrap(~insurance) +
labs(title = "Length of ICU Stay vs Insurance", x = "Length of Stay") +
theme_bw()`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Comment: Overall, the length of ICU stay in all insurance types tends to be relatively short and the distributions are all right-skewed. As the length of stay increases, the frequency decreases dramatically. The number of people using other insurances is the largest and that for Medicare insurance is almost the same, just a little bit smaller in the shortest los group. The number of people using Medicaid insurance is the smallest in all lengths of stay, accounting for only about one-tenth of the total of the other two types combined.
ggplot(mimic_icu_cohort, aes(los, fill = marital_status)) +
geom_histogram(color = "black") +
facet_wrap(~marital_status) +
labs(title = "Length of ICU Stay vs Martial Status", x = "Length of Stay") +
theme_bw()`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Comment: Overall, the length of ICU stay in all marital statuses tends to be relatively short and the distributions are all right-skewed. As the length of stay increases, the frequency decreases dramatically. The number of people who are married marital status remains the largest in almost all lengths of stay. In contrast, the number of people who are divorced and widowed marital status is small.
ggplot(mimic_icu_cohort, aes(los, fill = gender)) +
geom_histogram(color = "black") +
facet_wrap(~gender) +
labs(title = "Length of ICU Stay vs Gender", x = "Length of Stay") +
theme_bw()`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Comment: Overall, the length of ICU stay in all gender types tends to be relatively short and the distributions are all right-skewed. As the length of stay increases, the frequency decreases dramatically. The number of female is less than that for male remains in all length of stay groups.
ggplot(mimic_icu_cohort, aes(x = age_at_intime, y = los)) +
geom_point(alpha = 0.3) +
labs(title = "Length of ICU Stay vs Age at Intime", x = "Length of Stay") +
theme_bw()
Comment: The length of ICU stay tends to be relatively short for all ages and the distributions are all a little left-skewed. As the age increases, the length of stay tends to increase and the length of stay seems to be longer for people aged 65-80.
f1 <- ggplot(mimic_icu_cohort, aes(x = bicarbonate, y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
labs(y = "Length of Stay")
f2 <- ggplot(mimic_icu_cohort, aes(x = chloride, y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
labs(y = "Length of Stay")
f3 <- ggplot(mimic_icu_cohort, aes(x = creatinine, y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
labs(y = "Length of Stay")
f4 <- ggplot(mimic_icu_cohort, aes(x = glucose, y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
labs(y = "Length of Stay")
f5 <- ggplot(mimic_icu_cohort, aes(x = hematocrit, y = los)) +
geom_point(size = 0.5, alpha = 0.3)
f6 <- ggplot(mimic_icu_cohort, aes(x = potassium, y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
labs(y = "Length of Stay")
f7 <- ggplot(mimic_icu_cohort, aes(x = sodium, y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
labs(y = "Length of Stay")
f8 <- ggplot(mimic_icu_cohort, aes(x = wbc, y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
labs(y = "Length of Stay")
f <- grid.arrange(f1, f2, f3, f4, f5, f6, f7, f8, ncol = 2, nrow = 4,
top = "Length of ICU Stay vs
Last Available Lab Measurements before ICU Stay")Warning: Removed 9050 rows containing missing values (`geom_point()`).Warning: Removed 8883 rows containing missing values (`geom_point()`).Warning: Removed 5770 rows containing missing values (`geom_point()`).Warning: Removed 9099 rows containing missing values (`geom_point()`).Warning: Removed 5017 rows containing missing values (`geom_point()`).Warning: Removed 8901 rows containing missing values (`geom_point()`).Warning: Removed 8872 rows containing missing values (`geom_point()`).Warning: Removed 5094 rows containing missing values (`geom_point()`).
Comment: Bicarbonate and hematocrit exhibit relatively uniform distributions of length of ICU stay. Meanwhile, distributions of creatinine, glucose, and wbc are relatively concentrated in regions where measurement values are smaller, and as these values increase, length of stay tends to decrease. The distributions of Chloride, potassium, and sodium are similar to normal distribution.
g1 <- ggplot(mimic_icu_cohort, aes(x = heart_rate, y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
xlim(0, 250) +
labs(y = "Length of Stay")
g2 <- ggplot(mimic_icu_cohort, aes(x = non_invasive_blood_pressure_systolic,
y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
xlim(0, 300) +
labs(y = "Length of Stay")
g3 <- ggplot(mimic_icu_cohort, aes(x = non_invasive_blood_pressure_diastolic,
y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
xlim(0, 220) +
labs(y = "Length of Stay")
g4 <- ggplot(mimic_icu_cohort, aes(x = respiratory_rate, y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
xlim(0, 100) +
labs(y = "Length of Stay")
g5 <- ggplot(mimic_icu_cohort, aes(x = temperature_fahrenheit, y = los)) +
geom_point(size = 0.5, alpha = 0.3) +
xlim(0, 110) +
labs(y = "Length of Stay")
g <- grid.arrange(g1, g2, g3, g4, g5, ncol = 2, top = "Length of ICU Stay vs
First Vital Measurements within the ICU Stay")Warning: Removed 20 rows containing missing values (`geom_point()`).Warning: Removed 980 rows containing missing values (`geom_point()`).Warning: Removed 992 rows containing missing values (`geom_point()`).Warning: Removed 104 rows containing missing values (`geom_point()`).Warning: Removed 1363 rows containing missing values (`geom_point()`).
Comment: The distributions of heart_rate, non_invasive_blood_pressure_systolic, non_invasive_blood_pressure_diastolic, and respiratory_rate are almost uniform. temperature_fahrenheit around 35 tends to correlate with slightly shorter lengths of stay. Most values fall within the range of 90 to 105 Fahrenheit. Within this range, as temperature_fahrenheit increases, the length of stay tends to initially increase before decreasing. The longest lengths of stay occur around a temperature of approximately 98 Fahrenheit, which is the normal body temperature.
summary_by_care <- mimic_icu_cohort %>%
group_by(first_careunit) %>%
summarise(
mean_los = mean(los),
sd_los = sd(los),
median_los = median(los),
min_los = min(los),
max_los = max(los)
)
print(summary_by_care, width = Inf)# A tibble: 9 × 6
first_careunit mean_los sd_los median_los
<chr> <dbl> <dbl> <dbl>
1 Cardiac Vascular Intensive Care Unit (CVICU) 3.29 4.89 1.99
2 Coronary Care Unit (CCU) 3.19 4.00 2.01
3 Medical Intensive Care Unit (MICU) 3.26 4.61 1.83
4 Medical/Surgical Intensive Care Unit (MICU/SICU) 3.08 4.30 1.81
5 Neuro Intermediate 3.42 3.73 2.34
6 Neuro Stepdown 2.59 2.89 1.69
7 Neuro Surgical Intensive Care Unit (Neuro SICU) 6.30 7.46 3.65
8 Surgical Intensive Care Unit (SICU) 3.84 5.76 1.97
9 Trauma SICU (TSICU) 3.83 5.52 1.94
min_los max_los
<dbl> <dbl>
1 0.00352 103.
2 0.00125 66.6
3 0.00227 110.
4 0.00145 67.9
5 0.00346 43.5
6 0.0570 29.1
7 0.0216 59.6
8 0.00745 102.
9 0.00218 99.6ggplot(summary_by_care, aes(x = first_careunit, y = mean_los)) +
geom_bar(stat = "identity", fill = "salmon", width = 0.5) +
labs(title = "Mean ICU Length of Stay by First ICU Unit",
x = "First Careunit", y = "Mean Length of Stay") +
coord_flip() +
theme_bw()
Comment: The mean lengths of ICU stay for all first_icu groups are more than 2 days. The Neuro Surgical Intensive Care Unit (Neuro SICU) group has the highest mean length of stay, which is more than 6 days, while the Neuro Stepdown group has the lowest mean length of stay, which is around 2.5 days. The remaining seven categories are relatively similar.